Detecting Online Hate Speech Using Context Aware Models
This addresses the problem of online hate speech detection for social media platforms and moderators, but it is incremental as it builds on existing methods by adding context.
The paper tackles hate speech detection by incorporating context information, which is often overlooked, and shows that their proposed models outperform a strong baseline by 3-4% in F1 score, with a combined model improving performance by another 7%.
In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3% to 4% in F1 score and combining these two models further improve the performance by another 7% in F1 score.